A large global retailer approached my team at Board of Innovation to help them reimagine how their products are brought to life by harnessing the capabilities of Artificial Intelligence. In this project, I investigated how this AI technology can enhance product development for one of the top 3 big box retailers in the US.
To comply with a non-disclosure agreement, pieces of confidential information have been omitted and concealed in this case study.
The retailer had several critical needs: anticipating cultural trends, capturing more of the Gen Z market, accelerating time to market, and creating a breakthrough model to outperform fast-fashion giants while minimizing waste. They were looking for my team to deliver a pre-proof of concept for an AI fashion engine that would do all of these things while demonstrating the potential for operationalizing AI technology across lines of business.
As both a strategist and product designer, I played a critical role in this project by:
As part of a team made up of researchers, AI experts, and product designers, I helped lay out the vision for an AI-powered engine that can outpace the client company's existing innovation processes in speed, accuracy, and automation.
I conducted market research and trend analysis, researched emerging tools and tech, facilitated cross-functional brainstorming sessions, and performed technical and business feasibility studies to come up with a vision for the AI engine architecture. The result was a reimagined trend-to-product development cycle for a world that is more fluid, diverse, and volatile in the ways it consumes and creates goods.
I identified key levers that drive Gen Z consumer behavior through social listening and market research. Using these criteria, I developed a custom set of detailed personas that accurately represent the retailer's priority Gen Z target consumers. These personas were then integrated into the autonomous garment design engine, allowing for the prioritization of garments into cohesive micro drops throughout key moments in the design process. By inputting these personas into the machine learning model, the engine could ensure that the designs aligned with the preferences and behaviors of Gen Z consumers, resulting in more efficient and on-trend collections. I also helped design the synthetic persona feedback interface using Figma to simulate how fashion designers receive feedback, incorporating key points in from 1:1 interviews and social listening.
To design the prototype for the autonomous trend-to-product engine, I began by immersing myself in the real fashion design process through extensive expert interviews. These conversations with designers, design directors, merchandisers, planners, and suppliers helped me gain a deep understanding of end-user needs and workflows. My goal was to reflect and simulate the existing process while integrating current terminologies and frameworks, and to build in enhanced AI capabilities to streamline, elevate, and make the process more comprehensive.
Central to the design was ensuring user control and the ability to tweak inputs and designs, fostering a sense of ownership and creativity. I emphasized usability and intuitiveness, making sure the interface was accessible to all team members.
Collaborating closely with another designer on my team, we went through iterations of interactive wireframes and prototypes using Figma. This allowed us to continuously refine the interface based on feedback. Throughout this process, we worked closely with ML6 as our AI development partner, ensuring that our designs were technically feasible and functional. By considering the capabilities and constraints of AI, we were able to design an interface that seamlessly integrated advanced AI functionalities to assist with trend analysis, concept generation, and decision-making.
